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Few-Shot Learning

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하위 태스크: 12
평가표: 27
보존된 평가 기록: 52

Few-Shot Learning is an example of meta-learning, where a learner is trained on several related tasks, during the meta-training phase, so that it can generalize well to unseen (but related) tasks with just few examples, during the meta-testing phase. An effective approach to the Few-Shot Learning problem is to learn a common representation for various tasks and train task specific classifiers on top of this representation.

Source: Penalty Method for Inversion-Free Deep Bilevel Optimization

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/pavanbaswani/fincausal_sharedtask-2023

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Fine-grained Contract NER using instruction based model

https://github.com/pawn-sxy/dcama

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Dense Cross-Query-and-Support Attention Weighted Mask Aggregation for Few-Shot Segmentation

https://github.com/pbihao/hdmnet

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Hierarchical Dense Correlation Distillation for Few-Shot Segmentation

https://github.com/pedrohtg/weasel

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Learning to Segment Medical Images from Few-Shot Sparse Labels

https://github.com/pengbaolin/SC-GPT

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Few-shot Natural Language Generation for Task-Oriented Dialog

https://github.com/peustr/nfts-public

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Neural Fine-Tuning Search for Few-Shot Learning

https://github.com/pharmolix/openbiomed

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BioMedGPT: Open Multimodal Generative Pre-trained Transformer for BioMedicine

https://github.com/philipmit/thread

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THREAD: Thinking Deeper with Recursive Spawning

https://github.com/picsart-ai-research/mask-matching-transformer

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Mask Matching Transformer for Few-Shot Segmentation

https://github.com/pittnail/llms-vote

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Large Language Models Vote: Prompting for Rare Disease Identification

https://github.com/pku-marl/dexteroushands

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Towards Human-Level Bimanual Dexterous Manipulation with Reinforcement Learning

https://github.com/pku-ml/beyond_interpretability

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Beyond Interpretability: The Gains of Feature Monosemanticity on Model Robustness

https://github.com/pl97/fednlp

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An In-Depth Evaluation of Federated Learning on Biomedical Natural Language Processing

https://github.com/plai-group/simple-cnaps

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Improved Few-Shot Visual Classification

https://github.com/pldlgb/nuggets

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One-Shot Learning as Instruction Data Prospector for Large Language Models

https://github.com/pnborchert/core

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CORE: A Few-Shot Company Relation Classification Dataset for Robust Domain Adaptation

https://github.com/pnborchert/multirep

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Efficient Information Extraction in Few-Shot Relation Classification through Contrastive Representation Learning

https://github.com/pnnl/pychip_gui

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Design of a Graphical User Interface for Few-Shot Machine Learning Classification of Electron Microscopy Data

https://github.com/premiertaco/premier-taco

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Premier-TACO is a Few-Shot Policy Learner: Pretraining Multitask Representation via Temporal Action-Driven Contrastive Loss

https://github.com/primebo1/FAMNet

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FAMNet: Frequency-aware Matching Network for Cross-domain Few-shot Medical Image Segmentation

https://github.com/princeton-nlp/LM-BFF

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Making Pre-trained Language Models Better Few-shot Learners

https://github.com/princeton-nlp/metric-wsd

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Non-Parametric Few-Shot Learning for Word Sense Disambiguation

https://github.com/prinshul/prototype_neuralode_adv_attack

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Adversarially Robust Prototypical Few-shot Segmentation with Neural-ODEs

https://github.com/pris-cv/bi-frn

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Bi-directional Feature Reconstruction Network for Fine-Grained Few-Shot Image Classification

https://github.com/probcomp/solvingbib.jl

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Solving the Baby Intuitions Benchmark with a Hierarchically Bayesian Theory of Mind

https://github.com/probe2/kg_text

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Evaluating and Improving Graph to Text Generation with Large Language Models

https://github.com/psacfc/gpt4ts_adapter

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Understanding the Role of Textual Prompts in LLM for Time Series Forecasting: an Adapter View

https://github.com/psandovalsegura/adversarialquerying

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AutoProtoNet: Interpretability for Prototypical Networks

https://github.com/pulp-platform/eae-kws

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Boosting keyword spotting through on-device learnable user speech characteristics

https://github.com/pumpkinder/glip-cdfsod

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NTIRE 2025 Challenge on Cross-Domain Few-Shot Object Detection: Methods and Results

https://github.com/punyajoy/rgfs_ecai

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Rationale-Guided Few-Shot Classification to Detect Abusive Language

https://github.com/pxaris/fm-music-tagging

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Universal Music Representations? Evaluating Foundation Models on World Music Corpora

https://github.com/pxaris/lc-protonets

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LC-Protonets: Multi-Label Few-Shot Learning for World Music Audio Tagging

https://github.com/pytorch/fairseq/tree/main/examples/xglm

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Few-shot Learning with Multilingual Language Models

https://github.com/qcri/llmebench

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LLMeBench: A Flexible Framework for Accelerating LLMs Benchmarking

https://github.com/qcwthu/lifelong-fewshot-language-learning

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LFPT5: A Unified Framework for Lifelong Few-shot Language Learning Based on Prompt Tuning of T5

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